Overview

Dataset statistics

Number of variables20
Number of observations132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.8 KiB
Average record size in memory161.0 B

Variable types

NUM17
BOOL2
CAT1

Warnings

T(C) has constant value "132" Constant
nPortlandite has constant value "132" Constant
nAmor-Sl has constant value "132" Constant
Vol(aq) is highly correlated with b(H2O)High correlation
b(H2O) is highly correlated with Vol(aq)High correlation
nCa(s) is highly correlated with b(CaO) and 4 other fieldsHigh correlation
b(CaO) is highly correlated with nCa(s) and 4 other fieldsHigh correlation
nSi(s_reac) is highly correlated with b(SiO2) and 1 other fieldsHigh correlation
b(SiO2) is highly correlated with nSi(s_reac) and 1 other fieldsHigh correlation
mCSHQ is highly correlated with b(CaO) and 4 other fieldsHigh correlation
nCa(CSHQ) is highly correlated with b(CaO) and 4 other fieldsHigh correlation
nSi(CSHQ) is highly correlated with b(SiO2) and 1 other fieldsHigh correlation
nH2O(CSHQ) is highly correlated with b(CaO) and 4 other fieldsHigh correlation
C/S(CSHQ) is highly correlated with pH and 1 other fieldsHigh correlation
pH is highly correlated with C/S(CSHQ) and 1 other fieldsHigh correlation
nGelPW(CSH) is highly correlated with b(CaO) and 4 other fieldsHigh correlation
ratio is highly correlated with pH and 1 other fieldsHigh correlation
df_index has unique values Unique
b(CaO) has unique values Unique
b(SiO2) has unique values Unique
b(H2O) has unique values Unique
Vol(aq) has unique values Unique
pH has unique values Unique
nCa(aq) has unique values Unique
nCa(s) has unique values Unique
nSi(aq) has unique values Unique
nSi(s_reac) has unique values Unique
mCSHQ has unique values Unique
nCa(CSHQ) has unique values Unique
nSi(CSHQ) has unique values Unique
nH2O(CSHQ) has unique values Unique
C/S(CSHQ) has unique values Unique
nGelPW(CSH) has unique values Unique
ratio has unique values Unique

Reproduction

Analysis started2022-10-27 18:06:11.978940
Analysis finished2022-10-27 18:07:32.768236
Duration1 minute and 20.79 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.3636364
Minimum1
Maximum493
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:33.035247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.85
Q1135.25
median258
Q3376.25
95-th percentile455.35
Maximum493
Range492
Interquartile range (IQR)241

Descriptive statistics

Standard deviation140.8547123
Coefficient of variation (CV)0.5494332748
Kurtosis-1.126287006
Mean256.3636364
Median Absolute Deviation (MAD)118.5
Skewness-0.1713017587
Sum33840
Variance19840.04997
MonotocityStrictly increasing
2022-10-27T13:07:33.347760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
110.8%
 
33410.8%
 
37310.8%
 
37110.8%
 
36910.8%
 
36210.8%
 
36010.8%
 
35910.8%
 
35810.8%
 
35610.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
110.8%
 
210.8%
 
310.8%
 
710.8%
 
910.8%
 
ValueCountFrequency (%) 
49310.8%
 
49110.8%
 
49010.8%
 
48410.8%
 
48110.8%
 

T(C)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
25
132 
ValueCountFrequency (%) 
25132100.0%
 
2022-10-27T13:07:33.612525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-27T13:07:33.737535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:07:33.878158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

b(CaO)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5633417394
Minimum0.1572795
Maximum1.072483
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:34.145066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1572795
5-th percentile0.25480515
Q10.395828175
median0.54240155
Q30.7040071
95-th percentile0.94235661
Maximum1.072483
Range0.9152035
Interquartile range (IQR)0.308178925

Descriptive statistics

Standard deviation0.2158842854
Coefficient of variation (CV)0.3832208237
Kurtosis-0.6933774858
Mean0.5633417394
Median Absolute Deviation (MAD)0.1531214
Skewness0.3401987535
Sum74.3611096
Variance0.04660602468
MonotocityNot monotonic
2022-10-27T13:07:34.457560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.671535610.8%
 
0.417438510.8%
 
0.304548510.8%
 
0.371163510.8%
 
0.596764710.8%
 
0.520118510.8%
 
0.765046810.8%
 
0.434923810.8%
 
0.583108910.8%
 
0.524980910.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.157279510.8%
 
0.183949410.8%
 
0.207945910.8%
 
0.210133710.8%
 
0.221969610.8%
 
ValueCountFrequency (%) 
1.07248310.8%
 
1.04574110.8%
 
1.02501710.8%
 
0.999001110.8%
 
0.976054810.8%
 

b(SiO2)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4914465136
Minimum0.2005553
Maximum0.6984671
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:34.722994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2005553
5-th percentile0.236351095
Q10.38729075
median0.5159721
Q30.6120833
95-th percentile0.6806839
Maximum0.6984671
Range0.4979118
Interquartile range (IQR)0.22479255

Descriptive statistics

Standard deviation0.1425714285
Coefficient of variation (CV)0.2901056871
Kurtosis-0.9283578131
Mean0.4914465136
Median Absolute Deviation (MAD)0.10781525
Skewness-0.4499837831
Sum64.8709398
Variance0.02032661222
MonotocityNot monotonic
2022-10-27T13:07:35.035493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.477098410.8%
 
0.292059910.8%
 
0.244404710.8%
 
0.261639810.8%
 
0.374198710.8%
 
0.404127110.8%
 
0.625252110.8%
 
0.632890410.8%
 
0.520512610.8%
 
0.416195510.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.200555310.8%
 
0.202261410.8%
 
0.206963910.8%
 
0.208077910.8%
 
0.217604710.8%
 
ValueCountFrequency (%) 
0.698467110.8%
 
0.696884410.8%
 
0.692526110.8%
 
0.690760710.8%
 
0.685313310.8%
 

b(H2O)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.49008397
Minimum2.790351
Maximum8.271667
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:35.316745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.790351
5-th percentile3.10860925
Q14.1228855
median5.6032045
Q36.6570805
95-th percentile7.79609805
Maximum8.271667
Range5.481316
Interquartile range (IQR)2.534195

Descriptive statistics

Standard deviation1.543303557
Coefficient of variation (CV)0.2811074595
Kurtosis-1.147843083
Mean5.49008397
Median Absolute Deviation (MAD)1.35006
Skewness0.04185343313
Sum724.691084
Variance2.38178587
MonotocityNot monotonic
2022-10-27T13:07:35.599553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.32136810.8%
 
6.91481910.8%
 
4.82482810.8%
 
7.10746710.8%
 
4.42089810.8%
 
3.93710810.8%
 
6.2171510.8%
 
4.73905510.8%
 
6.16534410.8%
 
6.00187210.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
2.79035110.8%
 
2.86304310.8%
 
2.90092510.8%
 
2.95381610.8%
 
2.99581610.8%
 
ValueCountFrequency (%) 
8.27166710.8%
 
8.23733510.8%
 
8.20310910.8%
 
8.07976910.8%
 
7.9768410.8%
 

Vol(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07957414364
Minimum0.02489714
Maximum0.1328448
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:35.894879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.02489714
5-th percentile0.0374888125
Q10.05360864
median0.077098905
Q30.101883375
95-th percentile0.1221403
Maximum0.1328448
Range0.10794766
Interquartile range (IQR)0.048274735

Descriptive statistics

Standard deviation0.02813999351
Coefficient of variation (CV)0.3536323763
Kurtosis-1.090333879
Mean0.07957414364
Median Absolute Deviation (MAD)0.023845535
Skewness-0.004693084608
Sum10.50378696
Variance0.0007918592347
MonotocityNot monotonic
2022-10-27T13:07:36.207377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.110122510.8%
 
0.111205410.8%
 
0.0767855110.8%
 
0.116184210.8%
 
0.0607148510.8%
 
0.0535646310.8%
 
0.0860987810.8%
 
0.068018410.8%
 
0.0908528610.8%
 
0.0905997810.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.0248971410.8%
 
0.0291007910.8%
 
0.0299474510.8%
 
0.0305815210.8%
 
0.0345435210.8%
 
ValueCountFrequency (%) 
0.132844810.8%
 
0.132407610.8%
 
0.12705410.8%
 
0.124895710.8%
 
0.124312410.8%
 

pH
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.74151369
Minimum9.985236
Maximum12.45158
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:36.504254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.985236
5-th percentile10.5405665
Q111.434225
median11.85662
Q312.1741325
95-th percentile12.3842515
Maximum12.45158
Range2.466344
Interquartile range (IQR)0.7399075

Descriptive statistics

Standard deviation0.567607428
Coefficient of variation (CV)0.04834192959
Kurtosis0.9453564947
Mean11.74151369
Median Absolute Deviation (MAD)0.367095
Skewness-1.137613054
Sum1549.879807
Variance0.3221781924
MonotocityNot monotonic
2022-10-27T13:07:36.817065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.218510.8%
 
12.2452410.8%
 
11.9755610.8%
 
12.2303110.8%
 
12.4374110.8%
 
12.0416110.8%
 
11.9417110.8%
 
10.3077510.8%
 
11.78310.8%
 
12.0004910.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
9.98523610.8%
 
9.99221110.8%
 
10.1162310.8%
 
10.3077510.8%
 
10.3264310.8%
 
ValueCountFrequency (%) 
12.4515810.8%
 
12.4454110.8%
 
12.4374110.8%
 
12.4356510.8%
 
12.4187310.8%
 

nCa(aq)
Real number (ℝ≥0)

UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0004894765715
Minimum3.167933e-05
Maximum0.002278308
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:37.145190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.167933e-05
5-th percentile5.73710595e-05
Q10.00013548705
median0.00033168065
Q30.000676166475
95-th percentile0.00131318695
Maximum0.002278308
Range0.00224662867
Interquartile range (IQR)0.000540679425

Descriptive statistics

Standard deviation0.0004707325084
Coefficient of variation (CV)0.9617059034
Kurtosis2.271168668
Mean0.0004894765715
Median Absolute Deviation (MAD)0.00022144445
Skewness1.527883432
Sum0.06461090744
Variance2.215890945e-07
MonotocityNot monotonic
2022-10-27T13:07:37.457692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0011565710.8%
 
0.00125023610.8%
 
0.000441033710.8%
 
0.00125742110.8%
 
0.00112202610.8%
 
0.00036144410.8%
 
0.000455784610.8%
 
5.951412e-0510.8%
 
0.000331557910.8%
 
0.000552839410.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
3.167933e-0510.8%
 
3.362566e-0510.8%
 
4.289694e-0510.8%
 
4.675259e-0510.8%
 
5.356051e-0510.8%
 
ValueCountFrequency (%) 
0.00227830810.8%
 
0.00223388210.8%
 
0.00187049510.8%
 
0.00167599910.8%
 
0.0016087310.8%
 

nCa(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5628522598
Minimum0.1571865
Maximum1.071995
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:37.784353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1571865
5-th percentile0.25451292
Q10.3957059
median0.5422001
Q30.703792
95-th percentile0.941597595
Maximum1.071995
Range0.9148085
Interquartile range (IQR)0.3080861

Descriptive statistics

Standard deviation0.2156532366
Coefficient of variation (CV)0.383143592
Kurtosis-0.6938650501
Mean0.5628522598
Median Absolute Deviation (MAD)0.1520687
Skewness0.3391317342
Sum74.2964983
Variance0.04650631845
MonotocityNot monotonic
2022-10-27T13:07:38.096857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.67037910.8%
 
0.416188310.8%
 
0.304107510.8%
 
0.369906110.8%
 
0.595642710.8%
 
0.519757110.8%
 
0.76459110.8%
 
0.434864310.8%
 
0.582777310.8%
 
0.524428110.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.157186510.8%
 
0.183839410.8%
 
0.207773710.8%
 
0.210023910.8%
 
0.221814310.8%
 
ValueCountFrequency (%) 
1.07199510.8%
 
1.044810.8%
 
1.02340810.8%
 
0.998305710.8%
 
0.974832410.8%
 

nSi(aq)
Real number (ℝ≥0)

UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.009598411e-05
Minimum1.332651e-06
Maximum0.0003877878
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:38.409379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.332651e-06
5-th percentile2.59823785e-06
Q16.7147445e-06
median1.240896e-05
Q33.05661275e-05
95-th percentile0.00012206004
Maximum0.0003877878
Range0.000386455149
Interquartile range (IQR)2.3851383e-05

Descriptive statistics

Standard deviation4.980312627e-05
Coefficient of variation (CV)1.654809694
Kurtosis22.62729801
Mean3.009598411e-05
Median Absolute Deviation (MAD)7.62745e-06
Skewness4.126999501
Sum0.003972669903
Variance2.480351386e-09
MonotocityNot monotonic
2022-10-27T13:07:38.738259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.028254e-0610.8%
 
7.501703e-0610.8%
 
1.036035e-0510.8%
 
8.187083e-0610.8%
 
2.152951e-0610.8%
 
6.19351e-0610.8%
 
1.253787e-0510.8%
 
0.000131443310.8%
 
1.856863e-0510.8%
 
1.15432e-0510.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
1.332651e-0610.8%
 
1.569472e-0610.8%
 
1.591621e-0610.8%
 
1.718686e-0610.8%
 
2.020199e-0610.8%
 
ValueCountFrequency (%) 
0.000387787810.8%
 
0.000245878310.8%
 
0.000165553210.8%
 
0.000160651710.8%
 
0.000149696110.8%
 

nSi(s_reac)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4914164189
Minimum0.2005458
Maximum0.6984484
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:39.050774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2005458
5-th percentile0.236232685
Q10.38727915
median0.5159454
Q30.612065125
95-th percentile0.680671805
Maximum0.6984484
Range0.4979026
Interquartile range (IQR)0.224785975

Descriptive statistics

Standard deviation0.1425701151
Coefficient of variation (CV)0.2901207807
Kurtosis-0.9282724037
Mean0.4914164189
Median Absolute Deviation (MAD)0.1078887
Skewness-0.4500039729
Sum64.8669673
Variance0.02032623772
MonotocityNot monotonic
2022-10-27T13:07:39.378907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.477090410.8%
 
0.292052410.8%
 
0.244394310.8%
 
0.261631610.8%
 
0.374196510.8%
 
0.404120910.8%
 
0.625239610.8%
 
0.63275910.8%
 
0.52049410.8%
 
0.41618410.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.200545810.8%
 
0.202251910.8%
 
0.206943810.8%
 
0.208049310.8%
 
0.217599910.8%
 
ValueCountFrequency (%) 
0.698448410.8%
 
0.696883110.8%
 
0.692414710.8%
 
0.690735310.8%
 
0.685310510.8%
 

nPortlandite
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
132 
ValueCountFrequency (%) 
0132100.0%
 
2022-10-27T13:07:39.593139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

nAmor-Sl
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
132 
ValueCountFrequency (%) 
0132100.0%
 
2022-10-27T13:07:39.659141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

mCSHQ
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08063501364
Minimum0.02903427
Maximum0.1365326
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:39.850474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.02903427
5-th percentile0.0374229485
Q10.0618149525
median0.080735485
Q30.101590975
95-th percentile0.1206451
Maximum0.1365326
Range0.10749833
Interquartile range (IQR)0.0397760225

Descriptive statistics

Standard deviation0.02590449615
Coefficient of variation (CV)0.3212561762
Kurtosis-0.7527144553
Mean0.08063501364
Median Absolute Deviation (MAD)0.020230835
Skewness-0.03335075938
Sum10.6438218
Variance0.0006710429208
MonotocityNot monotonic
2022-10-27T13:07:40.148971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0883087310.8%
 
0.0545290210.8%
 
0.0420801310.8%
 
0.0486118310.8%
 
0.0749480910.8%
 
0.0709337610.8%
 
0.106582210.8%
 
0.0799607410.8%
 
0.0844249210.8%
 
0.0721834310.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.0290342710.8%
 
0.0297547210.8%
 
0.0316347410.8%
 
0.0323095610.8%
 
0.0345684310.8%
 
ValueCountFrequency (%) 
0.136532610.8%
 
0.13349910.8%
 
0.130960710.8%
 
0.129143810.8%
 
0.127630910.8%
 

nCa(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5628522598
Minimum0.1571865
Maximum1.071995
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:40.461463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1571865
5-th percentile0.25451292
Q10.3957059
median0.5422001
Q30.703792
95-th percentile0.941597595
Maximum1.071995
Range0.9148085
Interquartile range (IQR)0.3080861

Descriptive statistics

Standard deviation0.2156532366
Coefficient of variation (CV)0.383143592
Kurtosis-0.6938650501
Mean0.5628522598
Median Absolute Deviation (MAD)0.1520687
Skewness0.3391317342
Sum74.2964983
Variance0.04650631845
MonotocityNot monotonic
2022-10-27T13:07:40.768507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.67037910.8%
 
0.416188310.8%
 
0.304107510.8%
 
0.369906110.8%
 
0.595642710.8%
 
0.519757110.8%
 
0.76459110.8%
 
0.434864310.8%
 
0.582777310.8%
 
0.524428110.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.157186510.8%
 
0.183839410.8%
 
0.207773710.8%
 
0.210023910.8%
 
0.221814310.8%
 
ValueCountFrequency (%) 
1.07199510.8%
 
1.044810.8%
 
1.02340810.8%
 
0.998305710.8%
 
0.974832410.8%
 

nSi(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4914164189
Minimum0.2005458
Maximum0.6984484
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:41.081002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2005458
5-th percentile0.236232685
Q10.38727915
median0.5159454
Q30.612065125
95-th percentile0.680671805
Maximum0.6984484
Range0.4979026
Interquartile range (IQR)0.224785975

Descriptive statistics

Standard deviation0.1425701151
Coefficient of variation (CV)0.2901207807
Kurtosis-0.9282724037
Mean0.4914164189
Median Absolute Deviation (MAD)0.1078887
Skewness-0.4500039729
Sum64.8669673
Variance0.02032623772
MonotocityNot monotonic
2022-10-27T13:07:41.393541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.477090410.8%
 
0.292052410.8%
 
0.244394310.8%
 
0.261631610.8%
 
0.374196510.8%
 
0.404120910.8%
 
0.625239610.8%
 
0.63275910.8%
 
0.52049410.8%
 
0.41618410.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.200545810.8%
 
0.202251910.8%
 
0.206943810.8%
 
0.208049310.8%
 
0.217599910.8%
 
ValueCountFrequency (%) 
0.698448410.8%
 
0.696883110.8%
 
0.692414710.8%
 
0.690735310.8%
 
0.685310510.8%
 

nH2O(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.084928349
Minimum0.3527603
Maximum1.9176
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:41.722426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.3527603
5-th percentile0.49180056
Q10.830837425
median1.074232
Q31.36180675
95-th percentile1.69312525
Maximum1.9176
Range1.5648397
Interquartile range (IQR)0.530969325

Descriptive statistics

Standard deviation0.3696890452
Coefficient of variation (CV)0.3407497329
Kurtosis-0.7331963469
Mean1.084928349
Median Absolute Deviation (MAD)0.2831725
Skewness0.1219538295
Sum143.2105421
Variance0.1366699902
MonotocityNot monotonic
2022-10-27T13:07:42.034937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.22395810.8%
 
0.757273210.8%
 
0.574084910.8%
 
0.674343910.8%
 
1.05813710.8%
 
0.971719310.8%
 
1.45092710.8%
 
0.974493810.8%
 
1.13629910.8%
 
0.986314210.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.352760310.8%
 
0.385504910.8%
 
0.419046910.8%
 
0.434138310.8%
 
0.462501910.8%
 
ValueCountFrequency (%) 
1.917610.8%
 
1.87246210.8%
 
1.83572310.8%
 
1.80229510.8%
 
1.77295310.8%
 

C/S(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.147632458
Minimum0.6781719
Maximum1.60582
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:42.347438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.6781719
5-th percentile0.698857555
Q10.8999336
median1.1681565
Q31.37351725
95-th percentile1.5412337
Maximum1.60582
Range0.9276481
Interquartile range (IQR)0.47358365

Descriptive statistics

Standard deviation0.273929443
Coefficient of variation (CV)0.2386909163
Kurtosis-1.180382807
Mean1.147632458
Median Absolute Deviation (MAD)0.2390649
Skewness-0.1756603003
Sum151.4874845
Variance0.07503733976
MonotocityNot monotonic
2022-10-27T13:07:42.676200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.40514110.8%
 
1.42504710.8%
 
1.24433110.8%
 
1.41384310.8%
 
1.59179110.8%
 
1.28614210.8%
 
1.22287710.8%
 
0.687251110.8%
 
1.11966210.8%
 
1.26008710.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.678171910.8%
 
0.678314510.8%
 
0.681192110.8%
 
0.687251110.8%
 
0.687975610.8%
 
ValueCountFrequency (%) 
1.6058210.8%
 
1.59968610.8%
 
1.59179110.8%
 
1.59006310.8%
 
1.57364610.8%
 

nGelPW(CSH)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4189103258
Minimum0.1213689
Maximum0.8078792
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:42.988717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1213689
5-th percentile0.185571775
Q10.29989645
median0.4030448
Q30.521258
95-th percentile0.69957938
Maximum0.8078792
Range0.6865103
Interquartile range (IQR)0.22136155

Descriptive statistics

Standard deviation0.157830883
Coefficient of variation (CV)0.3767653202
Kurtosis-0.5231945889
Mean0.4189103258
Median Absolute Deviation (MAD)0.11041435
Skewness0.3757678093
Sum55.296163
Variance0.02491058764
MonotocityNot monotonic
2022-10-27T13:07:43.285582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.492937710.8%
 
0.30732110.8%
 
0.217934710.8%
 
0.272498210.8%
 
0.450795410.8%
 
0.374027310.8%
 
0.547330910.8%
 
0.337420210.8%
 
0.419798910.8%
 
0.376302610.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.121368910.8%
 
0.142875310.8%
 
0.154252410.8%
 
0.160156210.8%
 
0.165670510.8%
 
ValueCountFrequency (%) 
0.807879210.8%
 
0.78594710.8%
 
0.769150610.8%
 
0.744564510.8%
 
0.721232110.8%
 

ratio
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.148696006
Minimum0.6779379033
Maximum1.610132185
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2022-10-27T13:07:43.613709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.6779379033
5-th percentile0.6988372476
Q10.9002179195
median1.168888894
Q31.377052692
95-th percentile1.544073015
Maximum1.610132185
Range0.9321942816
Interquartile range (IQR)0.4768347725

Descriptive statistics

Standard deviation0.2748045765
Coefficient of variation (CV)0.239231768
Kurtosis-1.180444505
Mean1.148696006
Median Absolute Deviation (MAD)0.2393088952
Skewness-0.1733495746
Sum151.6278728
Variance0.07551755524
MonotocityNot monotonic
2022-10-27T13:07:43.926234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.40754108610.8%
 
1.42929070410.8%
 
1.24608282910.8%
 
1.41860489110.8%
 
1.5947802610.8%
 
1.28701712910.8%
 
1.22358133610.8%
 
0.687202397110.8%
 
1.12025895210.8%
 
1.26138052910.8%
 
Other values (122)12292.4%
 
ValueCountFrequency (%) 
0.677937903310.8%
 
0.678178692810.8%
 
0.680869531310.8%
 
0.687202397110.8%
 
0.687915360810.8%
 
ValueCountFrequency (%) 
1.61013218510.8%
 
1.604822110.8%
 
1.5947802610.8%
 
1.59191998810.8%
 
1.57572095310.8%
 

Interactions

2022-10-27T13:06:14.124094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:14.341635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:14.599251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:14.808908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:16.748864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:16.973126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:17.197124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:17.442671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:17.761386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:18.074706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:18.356090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:18.622803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:18.856183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:19.090451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:19.363699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:19.592976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:19.842721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:20.059593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:20.262064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:20.480794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:20.699639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:20.999359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:21.261367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:21.527052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:21.823065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:22.057070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:22.322067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:22.562059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:22.777792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:22.999985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:23.230895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:23.465214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:23.684327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:23.965169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:24.199700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:24.449561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:24.725292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:24.970280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:25.188282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:25.461229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:25.711210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:25.990215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:26.255678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:26.552522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:26.797594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:27.084668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:27.329304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:27.563506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:27.847842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:28.105846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:28.418355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:28.621976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:28.882485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:29.121590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:29.795115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:29.996789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:30.215230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:30.443152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:30.668635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:30.902716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:31.184842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:31.460438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:31.668478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:31.870059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:32.090582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:32.328488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:32.560467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:32.793605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:33.002791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:33.234797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:33.437119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:33.684317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:33.938672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:34.195166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:34.419270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:34.653041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:34.856984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:35.152774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:35.371523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:35.607355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:35.829258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:36.146253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:36.443256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:36.743045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:37.028247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:37.273926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:37.514915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:37.731196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:37.996866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:06:38.226099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-10-27T13:07:31.045943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-10-27T13:07:44.254354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-27T13:07:44.817440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-27T13:07:45.379926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-27T13:07:45.926914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-27T13:07:31.609351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:07:32.406256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
0125.00.6715360.4770987.3213680.11012212.218500.0011570.6703790.0000080.4770900.00.00.0883090.6703790.4770901.2239581.4051410.4929381.407541
1225.00.3750900.2693275.8236790.09280812.196570.0009220.3741680.0000070.2693200.00.00.0495060.3741680.2693200.6850751.3893050.2742151.392692
2325.00.6920740.4740022.8630430.02910112.289120.0003660.6917080.0000020.4740010.00.00.0898180.6917080.4740011.2516221.4592980.5144041.460065
3725.00.5451850.6676507.7728790.11930111.246180.0001540.5450310.0000690.6675810.00.00.0917500.5450310.6675811.1698110.8164270.4323010.816573
4925.00.2852680.2978513.4670410.05209511.535020.0001110.2851560.0000170.2978340.00.00.0443990.2851560.2978340.5835830.9574350.2153840.957752
51325.00.9474300.6664077.6341580.10675212.238480.0011800.9462500.0000070.6664000.00.00.1241480.9462500.6664001.7232291.4199430.6979751.421698
62025.00.8448050.5999144.1786220.04763912.221550.0005040.8443010.0000030.5999100.00.00.1111510.8443010.5999101.5408971.4073780.6211191.408211
72725.00.1839490.2080784.0447040.06611211.402650.0001100.1838390.0000290.2080490.00.00.0297550.1838390.2080490.3855050.8836340.1428750.884041
82925.00.5634270.5547805.6269620.08120811.626290.0002100.5632180.0000230.5547570.00.00.0853100.5632180.5547571.1320201.0152510.4165341.015588
93225.00.5523120.3578654.7772900.06846312.383230.0010980.5512130.0000030.3578620.00.00.0701710.5512130.3578620.9857491.5402950.4155091.543352

Last rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
12244925.00.4293360.4618546.3773420.09920611.487360.0001930.4291430.0000370.4618170.00.00.0677800.4291430.4618170.8863120.9292480.3277350.929593
12345125.00.6799460.6789604.4120820.05492011.604790.0001350.6798100.0000160.6789440.00.00.1036370.6798100.6789441.3722251.0012750.5051781.001451
12445425.00.5091270.6925267.5169970.11550810.909140.0001070.5090200.0001110.6924150.00.00.0903970.5090200.6924151.1240050.7351380.4042610.735174
12545725.00.4961220.4486693.0579490.03770411.761760.0001310.4959910.0000080.4486610.00.00.0722630.4959910.4486610.9709141.1054900.3581571.105763
12645925.00.8274530.6335456.6116760.09162112.071180.0006650.8267880.0000100.6335350.00.00.1121610.8267880.6335351.5393251.3050380.5965401.306067
12748125.01.0724830.6968843.6112320.03058212.381020.0004881.0719950.0000010.6968830.00.00.1365331.0719950.6968831.9176001.5382710.8078791.538968
12848425.00.3186440.2176055.0986880.08169912.289540.0010290.3176160.0000050.2176000.00.00.0412390.3176160.2176000.5746851.4596320.2362181.464327
12949025.00.8369750.5629325.6767140.07530412.320360.0010270.8359490.0000040.5629280.00.00.1078450.8359490.5629281.5066971.4850010.6247591.486814
13049125.00.8679940.6030462.9538160.02489712.263270.0002930.8677000.0000020.6030440.00.00.1132700.8677000.6030441.5752111.4388670.6425891.439349
13149325.00.3128350.4356033.6624450.05362310.776440.0000470.3127880.0000610.4355420.00.00.0562220.3127880.4355420.6945740.7181580.2470520.718165